Data is the genesis of informed enterprise decisions. Today, organizations collect and monetize data. Data Lake gives direction to the overflow of data. Raising the standard of the existing business infrastructure, accelerating process implementation, eliminating the data silos, and maximizing the value of obtained data are the business goals of many organizations by default.
Generally, financial institutions offer services that cover commercial banking, private banking, personal services, and investment banking. Though the data warehouse catered to these service portfolios, somewhere, the intelligence was lacking. Moreover, the existing solution took too long to offer insightful results out of the data collected. With the data volume increasing each passing day, challenges just grew.
Today, centralized data storage with unparalleled processing speed is the utmost requirement to ensure a barrier-free data paradigm. In this era, financial institutions are making business intelligence and analytics-driven moves to cope with their data-related issues. To derive value out of the data, setups need to be robust, efficient, and productive.
The implementation of the Data Lake solution triggers enterprise-level transformation by bringing more agility to data exploration and analytical practices. At an industry level, organizations get an influx of data every second. Putting this data to purposeful uses will yield benefits that have a long-lasting business impact. With the integrated implementation of Business Intelligence & Analytics Solutions, the Hadoop-based Data Lake Solution gets aligned with institutional data and draws insightful reports out of them.
With a Data Lake, intelligent access to data helps yield outcomes that are business-driven and customer-focused. The smart solution not only describes the current scenario, but also predicts and prescribes the most optimized and feasible ways to solve business challenges. The Big Data and Advanced Analytics Solutions have brought digital transformation across major industries. The banking and insurance sector have benefited a great deal from it. Problem areas like risk assessment, fraud detection, portfolio management, and wealth management are no longer a big challenge for institutions. With an advanced framework and robust architecture, issue reporting has been simplified. Even the trained Machine Learning Models help find roadblocks that usually get unnoticed.
|Can easily work with raw data||Need processed data to operate|
|Accessibility and real-time updating||More complicated when compared to data lake|
|Can store data in raw form||Stores data extracted from transactional source|
|Faster processing time||Not as fast as Data Lake|
|Less storage cost as compared to data warehouse||Comparatively more expensive|
|Offers high agility with ease of data capture||Though competitively good, lacks in agility|
In many circumstances, it's the decision that plays a pivotal role in shaping the future course of action. It's a common agenda for industries like banking and finance to deal with events and incidents whose outcomes depend on the pillar of decisions. The organizational move to go for the data lake solution is one of those best fitting decisions that set the user apart from its competitors.
Big Data / Technology
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November 26, 2019
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